Why CEOs Are Getting AI Wrong — with Ethan Mollick
February 12, 2026
AI Summary
5 min read🎙️ The Speakers & Context
- The Format: Interview.
- The Key Players:
- Host: Scott Galloway, NYU Stern professor, podcaster (Prof G Pod), serial entrepreneur; brings contrarian investor lens, critiques Big Tech via "Resist and Unsubscribe" campaign.
- Guest: Ethan Mollick, Wharton professor, author of Co-Intelligence; credible via AI research (e.g., BCG trials showing 40% productivity gains), Substack (One Useful Thing), frequent AI lab/policy convos.
- The Vibe: Activist-to-technical; Galloway's anti-Big Tech fervor shifts to pragmatic AI optimism vs. Mollick's balanced "nitty-gritty" focus.
🎣 The Executive Hook
- The "One Big Idea": AI's jagged frontier demands CEO-led experimentation over top-down mandates—pair universal tool access with "leadership labs" to harvest employee use cases, yielding 40%+ productivity in coding/managerial tasks while avoiding commoditization traps.
- Why It Matters: Amid scaling laws driving exponential gains (e.g., 100% AI-written code at labs), firms ignoring this face labor mismatches; justifies trillion-dollar valuations only if efficiencies expand markets, not just cut costs—echoing Fed-tightening scrutiny on tech capex.
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What you'll learn
- 1 (00:00) **🎙️ Introduction: Ethan Mollick**
- 2 (01:08) **Resist and Unsubscribe Campaign Update**
- 3 (12:47) **Dario Amodei's AI Warnings**
- 4 (13:56) **AI Risks: Existential vs. Practical**
- 5 (15:52) **AI Productivity at Work**
- 6 (19:12) **Jagged Frontier & CEO Deployment Advice**
- 7 (21:46) **Agentic AI Explained**
+ Full timestamped outline available in the app
Show Notes
Ethan Mollick, professor at the Wharton School and author of One Useful Thing, joins Scott Galloway to examine the biggest mistake companies are making about AI.
They discuss why fears of mass job loss may be premature, how quiet productivity gains are already reshaping work, and why most organizations lack the imagination to redesign themselves around new technology. Ethan also explores AI in higher education and medicine, the rise of open-weight models, and what all of this means for young people entering the workforce.
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